Does the performance of a model increase if dropout is disabled at evaluation time?
Dropout is a technique that helps to avoid overfitting during training. That is, one can use dropout only for training.


  units may change in a way that they fix up the mistakes of the other
  units. This may lead to complex co-adaptations. This, in turn, leads to
  overfitting because these co-adaptations do not generalize to unseen
  data.


If you want to evaluate your model, you should turn off all dropout layers. For example, PyTorch's model.eval() does this work.

More about dropout:


Improving neural networks by preventing co-adaptation of feature detectors
Dropout: A Simple Way to Prevent Neural Networks from Overfitting